AI Term:Long Short-Term Memory (LSTM)

Long Short-Term Memory (LSTM) networks are a special kind of Recurrent Neural Network (RNN) designed to overcome the limitations of traditional RNNs, particularly in learning long-term dependencies in data sequences.

You can think of an LSTM as a smart secretary who not only keeps track of the ongoing tasks but also remembers important events from the past, discards irrelevant ones, and makes decisions about what information to store or throw away.

Here’s a more detailed explanation: An LSTM network has a similar structure to an RNN, with the difference being that each neuron has a cell state and three “gates”: an input gate, a forget gate, and an output gate.

1. The input gate decides how much of the new information (current input) should be stored in the cell state.
2. The forget gate decides what portion of the existing memory (previous cell state) should be kept or discarded.
3. The output gate decides what information should be outputted at the current time step.

These gates allow LSTMs to effectively learn and remember information over long sequences, making them particularly useful for tasks such as language modeling, text generation, and machine translation, where understanding the long-term context is crucial.